This invention relates to classification systems, e.g. speaker recognition systems, and more specifically to a method and apparatus for iterative training of a classification system.
Modern classification systems require high accuracy training for optimal performance in a variety of environments. One method of achieving high accuracy is through discriminative training methods. A discriminative polynomial classifier for speaker verification is described in detail in W. M. Campbell and K. T. Assaleh, xe2x80x9cPolynomial Classifier Techniques for Speaker Verificationxe2x80x9d, in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, pp. 321-324, 1999. Iterative techniques to solve linear equations have typically been used in two areas. In the numerical analysis community, methods are targeted toward solving large sparse systems. In the engineering community, approaches have concentrated on using iterative methods for recursive learning. The present disclosure applies to both areas.
Polynomial discriminative training methods optimize the performance of a classifier by maximally separating the decision regions. The main advantages of this polynomial approach are:
the training method is able to handle large amounts of enrollment data in a straightforward manner;
the architecture is based upon a simple multiply-add only architecture;
the classifier is trained discriminatively with an algorithm achieving the global minimum; and
the classifier output approximates a posteriori probabilities, which eliminates the need to perform cohort selection and cohort scoring (cohorts are incorporated as part of the training).
A major difficulty in using polynomial discriminative training for previous systems is the large memory footprint required for training. The training process requires the solution of a large (for small platforms) matrix problem. This is a serious drawback for portable devices, sometimes prohibiting discriminative training from being a viable choice. Many portable devices (e.g., cell phones) have high MIPS (i.e., they include DSPs and the like) but little memory. Therefore, it is desirable to construct methods and apparatus that minimize memory usage and produce equivalent functionality.
Accordingly the present disclosure describes a new and improved method and apparatus for iterative training of a classification system in which memory usage is substantially reduced while producing equivalent functionality.